Changing views in Canadian geomorphology: are we seeing the landscape for the processes?
Bibliographic record
Abstract
Geomorphology in Canada, as elsewhere, has evolved into an essentially bipartite discipline focusing either on ‘process’ or broader ‘historical’ (Quaternary) landscape interpretation. A growing emphasis on process‐oriented research that relies increasingly on instrumentation and computational technologies has occurred. Critics of such research note limited applicability for landscape evolution, fashionability of methods and limited societal relevance. Indeed, some say we are not seeing the landscape for the processes. This article discusses the changing nature of geomorphology since the Quantitative Revolution of the 1950s including new advances, recent trends and challenges. Publication trends and recent advances suggest that the discipline is very healthy (following a slump in the early 1990s) and continues to evolve, which may reflect increasing research infrastructure and/or funding opportunities and new publications spotlighting Canadian research. Unfortunately, fundamental (less applied) research is threatened by funding program shifts, changing institutional pressures and a decline in research capacity from retirement attrition, and student recruitment challenges. Three research priorities are recommended: (1) continued fundamental research, (2) more integrated modelling to link micro scale processes to macro scale landform behaviour and (3) improvements in profiling our discipline amongst students and related professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".